title: Doubly robust estimation of causal effects.
abstract: Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/~mfunk/dr/.
authors.affiliation: Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. mfunk@unc.edu
authors.forename: Michele Jonsson
authors.initials: MJ
authors.lastname: Funk
authors.forename: Daniel
authors.initials: D
authors.lastname: Westreich
authors.forename: Chris
authors.initials: C
authors.lastname: Wiesen
authors.forename: Til
authors.initials: T
authors.lastname: Stürmer
authors.forename: M Alan
authors.initials: MA
authors.lastname: Brookhart
authors.forename: Marie
authors.initials: M
authors.lastname: Davidian
mesh_terms: D015984:Causality; D003198:Computer Simulation; D016001:Confidence Intervals; D015986:Confounding Factors, Epidemiologic; D004812:Epidemiologic Methods; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D057216:Propensity Score; D012044:Regression Analysis